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International Conference on Mechanical, Control and Computer engineering (ICMCCE)

作者： Yan Wang Pujia Wang Boyang Sun Kai He Lan Huang School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Ministry of Education Changchun China

Generative Adversarial Networks (GANs) have achieved huge success in some unsupervised learning fields. There is no doubt that clustering takes a lot of weight in unsupervised algorithm. And in this paper, we raise the Improved Information Maximizing Generative Adversarial Networks (IInfoGAN) algorithm for learning discriminative classifiers from unlabeled data. The basis of our method is an math function that contains the Mutual Information (MI) and Cross Entropy of the observed examples and their predicted classification category distribution, thus enhancing the robustness of the classifier to adversarial generative models. Experiments show that the interpretable representation learned by IInfoGAN is competitive with the representation learned by existing unsupervised methods.

关键词： Training Time series analysis Clustering algorithms Predictive models Generative adversarial networks Prediction algorithms Entropy

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Curvature regularization to prevent distortion in graph embedding 20

Curvature regularization to prevent distortion in graph embe...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Hongbin Pei Bingzhe Wei Kevin Chen-Chuan Chang Chunxu Zhang Bo Yang College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign and Department of Computer Science University of Illinois at Urbana-Champaign

ISBN: (纸本)9781713829546

Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding space. We argue an important but neglected problem about this proximity-preserving strategy: Graph topology patterns, while preserved well into an embedding manifold by preserving proximity, may distort in the ambient embedding Euclidean space, and hence to detect them becomes difficult for machine learning models. To address the problem, we propose curvature regularization, to enforce flatness for embedding manifolds, thereby preventing the distortion. We present a novel angle-based sectional curvature, termed ABS curvature, and accordingly three kinds of curvature regularization to induce flat embedding manifolds during graph embedding. We integrate curvature regularization into five popular proximity-preserving embedding methods, and empirical results in two applications show significant improvements on a wide range of open graph datasets.

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Pyramid Pooling Dense Convolutional Neural Network for Multi-focus Image Fusion

Pyramid Pooling Dense Convolutional Neural Network for Multi...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Yi Li Xuanjing Shen Haipeng Chen College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

In recent years, image fusion becomes one of the important technique in information fusion fields. In this paper, we proposed a Deep Learning-based pyramid pooling dense convolutional neural network for multi-focus image fusion problem. The PDenseCNN we proposed can accurately detect the clear regions and generate a fused decision feature map, afterward, morphological operation and guided filter are used to optimize the initial map. Finally, it can obtain a clear fusion image based on weighted-sum strategy. The experimental results proved that the proposed method can obtain accurate decision maps and state-of-art fusion results on both visual effect and objective evaluation assessment.

关键词： Economic indicators

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Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

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Journal of Computer Science & Technology 2018年第4期33卷 668-681页

作者： Lin Yue Xiao-Xin Sun Wen-Zhu Gao Guo-Zhong Feng Bang-Zuo Zhang School of Information Science and Technology Northeast Normal University Changchun 130117 China Key Laboratory of Applied Statistics of Ministry of Education Northeast Normal University Changchun 130024 China School of Environment Northeast Normal University Changchun 130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder （DAE）, called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.

关键词： semantic representation plot information denoising autoencoder collaborative filtering auxiliary information

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Parking Space Prediction Algorithm Based on Recurrent Neural Network and Ensemble Learning Algorithm

Parking Space Prediction Algorithm Based on Recurrent Neural...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Kedi Lv Haipeng Chen Yingda Lv Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Center for Computer Fundamental Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

The essence of parking space prediction is a practical application of time series prediction problem. It provides effective auxiliary information for traffic management and personal travel. As a new demand, they face challenges in data acquisition, accuracy and robustness. The parking space prediction algorithm based on recurrent neural network (RNN) and ensemble learning algorithm is proposed in this paper. However, because the neural network needs large data training, the lack of parking space data in specific areas is a common challenge for researchers. In this paper, a new network model structure based on ensemble learning algorithm is proposed, which is called E-RNN. The ensemble learning algorithm model is used as the primary learner and the neural network as the secondary learner for the ensemble learning. By using the adaptability of the algorithm in ensemble learning to the collective data, the neural network can get better prediction effect under the training of small data sets. Finally, the parameters of the new model are optimized by using particle swarm optimization (PSO) algorithm, and a better model is obtained. The experimental results show that the algorithm has high accuracy and robustness for parking space prediction with a small data sets.

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Motion Deblur of QR Code Based on Generative Adversative Network

Motion Deblur of QR Code Based on Generative Adversative Net...

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2019第二届算法、计算和人工智能国际会议

作者： Benzhi Wang Jingben Xu Junke Zhang Guancheng Li Xin Wang College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University

Aiming at the problem that the QR codes cannot be recognized due to factors such as motion blur caused by the hand tremor and environmental noise in the QR codes of the packaged product, this paper proposes an algorithm based on the Generative Adversative Network to remove QR codes motion blur. Compared with the traditional deblurring algorithm, this method eliminates the process of blur kernel estimation, directly restores the motion blurred image. Gradient L1 regularization is added on the basis of the traditional GAN, which enhances the edge features of the restored image and adds the perceptual loss term, so that the image generated by the generator is closer to the global structure of the target clear image. The results show that the generator can remove the blur and generate a QR code with high recognition rate.

关键词： Generative Adversative Network Motion Blur, QR Code, Perceived Loss Function

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AR Marketing Application Based on iOS Platform

AR Marketing Application Based on iOS Platform

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作者： Zheng Zhang Xin Wang Hongchen Jiang Yuemeng Hu Jiayu Du Qiang Zhang College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University

Due to the development of computer vision, in recent years, augmented reality grows rapidly. A lot of augmented technologies have been applied in actual problems. At present, the industry of traditional marketing requires a lot of manpower, and the application of AR technology in marketing industry can greatly reduce industrial costs. In this paper, an Augmented Reality Marketing Application based on iOS platform is designed. Users can use this APP to scan physical stores signboards, which are saved previously, to trigger a 3D stereo model combined with the real world. At the same time, through the interaction with the model, users can get the business information and its sales activities, thus enhancing the consumer interest in the physical *** experiment results show this application work feasible and reliable. It is an attempt to integrate online and offline economy.

关键词： Augmented Reality Entity marketing Image Identification Online and offline technique

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An Unmanned Aerial Vehicle Pose Estimation System Guided by Desired Shot

An Unmanned Aerial Vehicle Pose Estimation System Guided by ...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Xinglei Dou Qijian Ding Qiao Du Min Yang Yuzhou Huo Xin Wang College of Software Engineering Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728150307

ISBN: (纸本)9781728150314

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid.

关键词： Three-dimensional displays Feature extraction Unmanned aerial vehicles Cameras Pose estimation Software engineering Data mining

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A pseudo label based dataless naive bayes algorithm for text classification with seed words 27

A pseudo label based dataless naive bayes algorithm for text...

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27th International Conference on computational Linguistics, COLING 2018

作者： Li, Ximing Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781948087506

Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset. © 2018 COLING 2018 - 27th International Conference on computational Linguistics, Proceedings. All rights reserved.

关键词： Iterative methods

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Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications

arXiv

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arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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